Jai Won Chung

Screening for Prediabetes Using Machine Learning Models

The global prevalence of diabetes is rapidly increasing. Studies support the necessity of screening and interventions for prediabetes, which could result in serious complications and diabetes. This study aimed at developing an intelligence-based screening model for prediabetes. Data from the Korean National Health and Nutrition Examination Survey (KNHANES) were used, excluding subjects with diabetes. The KNHANES 2010 data (n = 4685) were used for training and internal validation, while data from KNHANES 2011 (n = 4566) were used for external validation. We developed two models to screen for prediabetes using an artificial neural network (ANN) and support vector machine (SVM) and performed a systematic evaluation of the models using internal and external validation. We compared the performance of our models with that of a screening score model based on logistic regression analysis for prediabetes that had been developed previously. The SVM model showed the areas under the curve of 0.731 in the external datasets, which is higher than those of the ANN model (0.729) and the screening score model (0.712), respectively. The prescreening methods developed in this study performed better than the screening score model that had been developed previously and may be more effective method for prediabetes screening.

Prediction of ATLS hypovolemic shock class in rats using the perfusion index and lactate concentration.

ABSTRACT It is necessary to quickly and accurately determine Advanced Trauma Life Support (ATLS) hemorrhagic shock class for triage in cases of acute hemorrhage caused by trauma. However, the ATLS classification has limitations, namely, with regard to primary vital signs. This study identified the optimal variables for appropriate triage of hemorrhage severity, including the peripheral perfusion index and serum lactate concentration in addition to the conventional primary vital signs. To predict the four ATLS classes, three popular machine learning algorithms with four feature selection methods for multicategory classification were applied to a rat model of acute hemorrhage. A total of 78 anesthetized rats were divided into four groups for ATLS classification based on blood loss (in percent). The support vector machine one-versus-one model with the Kruskal-Wallis feature selection method performed best, with 80.8% accuracy, relative classifier information of 0.629, and a kappa index of 0.732. The new hemorrhage-induced severity index (lactate concentration/perfusion index), diastolic blood pressure, mean arterial pressure, and the perfusion index were selected as the optimal variables for predicting the four ATLS classes by support vector machine one-versus-one with the Kruskal-Wallis method. These four variables were also selected for binary classification to predict ATLS classes I and II versus III and IV for blood transfusion requirement. The suggested ATLS classification system would be helpful to first responders by indicating the severity of patients, allowing physicians to prepare suitable resuscitation before hospital arrival, which could hasten treatment initiation.

Multicategory classification of 11 neuromuscular diseases based on microarray data using support vector machine

We applied multicategory machine learning methods to classify 11 neuromuscular disease groups and one control group based on microarray data. To develop multicategory classification models with optimal parameters and features, we performed a systematic evaluation of three machine learning algorithms and four feature selection methods using three-fold cross validation and a grid search. This study included 114 subjects of 11 neuromuscular diseases and 31 subjects of a control group using microarray data with 22,283 probe sets from the National Center for Biotechnology Information (NCBI). We obtained an accuracy of 100%, relative classifier information (RCI) of 1.0, and a kappa index of 1.0 by applying the models of support vector machines one-versus-one (SVM-OVO), SVM one-versus-rest (OVR), and directed acyclic graph SVM (DAGSVM), using the ratio of genes between categories to within-category sums of squares (BW) feature selection method. Each of these three models selected only four features to categorize the 12 groups, resulting in a time-saving and cost-effective strategy for diagnosing neuromuscular diseases. In addition, a gene symbol, SPP1 was selected as the top-ranked gene by the BW method. We confirmed relationships between the gene (SPP1) and Duchenne muscular dystrophy (DMD) from a previous study. With our models as clinically helpful tools, neuromuscular diseases could be classified quickly using a computer, thereby giving a time-saving, cost-effective, and accurate diagnosis.

Exposure of surgeons to extremely low-frequency magnetic fields during laparoscopic and robotic surgeries.

AbstractThe development of new medical electronic devices and equipment has increased the use of electrical apparatuses in surgery. Many studies have reported the association of long-term exposure to extremely low-frequency magnetic fields (ELF-MFs) with diseases or cancer. Robotic surgery has emerged as an alternative tool to overcome the disadvantages of conventional laparoscopic surgery. However, there has been no report regarding how much ELF-MF surgeons are exposed to during laparoscopic and robotic surgeries. In this observational study, we aimed to measure and compare the ELF-MFs that surgeons are exposed to during laparoscopic and robotic surgery.The intensities of the ELF-MFs surgeons are exposed to were measured every 4 seconds for 20 cases of laparoscopic surgery and 20 cases of robotic surgery using portable ELF-MF measuring devices with logging capability.The mean ELF-MF exposures were 0.6 ± 0.1 mG for laparoscopic surgeries and 0.3 ± 0.0 mG for robotic surgeries (significantly lower with P < 0.001 by Mann–Whitney U test).Our results show that the ELF-MF exposure levels of surgeons in both robotic and conventional laparoscopic surgery were lower than 2 mG, which is the most stringent level considered safe in many studies. However, we should not overlook the effects of long-term ELF-MF exposure during many surgeries in the course of a surgeons career.

Classification of serous ovarian tumors based on microarray data using multicategory support vector machines

Ovarian cancer, the most fatal of reproductive cancers, is the fifth leading cause of death in women in the United States. Serous borderline ovarian tumors (SBOTs) are considered to be earlier or less malignant forms of serous ovarian carcinomas (SOCs). SBOTs are asymptomatic and progression to advanced stages is common. Using DNA microarray technology, we designed multicategory classification models to discriminate ovarian cancer subclasses. To develop multicategory classification models with optimal parameters and features, we systematically evaluated three machine learning algorithms and three feature selection methods using five-fold cross validation and a grid search. The study included 22 subjects with normal ovarian surface epithelial cells, 12 with SBOTs, and 79 with SOCs according to microarray data with 54,675 probe sets obtained from the National Center for Biotechnology Information gene expression omnibus repository. Application of the optimal model of support vector machines one-versus-rest with signal-to-noise as a feature selection method gave an accuracy of 97.3%, relative classifier information of 0.916, and a kappa index of 0.941. In addition, 5 features, including the expression of putative biomarkers SNTN and AOX1, were selected to differentiate between normal, SBOT, and SOC groups. An accurate diagnosis of ovarian tumor subclasses by application of multicategory machine learning would be cost-effective and simple to perform, and would ensure more effective subclass-targeted therapy.

Intraoperative Diagnosis Support Tool for Serous Ovarian Tumors Based on Microarray Data Using Multicategory Machine Learning.

Objectives Serous borderline ovarian tumors (SBOTs) are a subtype of serous ovarian carcinoma with atypical proliferation. Frozen-section diagnosis has been used as an intraoperative diagnosis tool in supporting the fertility-sparing surgery by diagnosing SBOTs with accuracy of 48% to 79%. Using DNA microarray technology, we designed multicategory classification models to support frozen-section diagnosis within 30 minutes. Materials and Methods We systematically evaluated 6 machine learning algorithms and 3 feature selection methods using 5-fold cross-validation and a grid search on microarray data obtained from the National Center for Biotechnology Information. To validate the models and selected biomarkers, expression profiles were analyzed in tissue samples obtained from the Yonsei University College of Medicine. Results The best accuracy of the optimal machine learning model was 97.3%. In addition, 5 features, including the expression of the putative biomarkers SNTN and AOX1, were selected to differentiate between normal, SBOT, and serous ovarian carcinoma groups. Different expression levels of SNTN and AOX1 were validated by real-time quantitative reverse-transcription polymerase chain reaction, Western blotting, and immunohistochemistry. A multinomial logistic regression model using SNTN and AOX1 alone was used to construct a simple-to-use equation that gave a diagnostic test accuracy of 91.9%. Conclusions We identified 2 biomarkers, SNTN and AOX1, that are likely involved in the pathogenesis and progression of ovarian tumors. An accurate diagnosis of ovarian tumor subclasses by application of the equation in conjunction with expression analysis of SNTN and AOX1 would offer a new accurate diagnosis tool in conjunction with frozen-section diagnosis within 30 minutes.

Safety of Exposure From Extremely Low Frequency Magnetic Fields During Prenatal Ultrasound Examinations in Clinicians and Pregnant Women

Abstract Investigations into the safety of ultrasonography in pregnancy have focused on the potential harm of ultrasound itself. However, no data have been published regarding the electromagnetic fields that ultrasound devices might produce. This study is the first to measure extremely low-frequency magnetic field (ELF-MF) exposure of clinicians and pregnant women during prenatal ultrasound examinations in the examination room from 2 different ultrasound devices and compare them with ELF-MFs during patient consultation in the consulting room. The ELF-MF intensities that clinicians and pregnant women were exposed to were measured every 10 seconds for 40 prenatal ultrasound examinations using Philips iU22 or Accuvix V20 Prestige machines and 20 patient consultations in a consulting room using portable ELF-MF measurement devices. The mean ELF-MF exposure of both clinicians and pregnant women was 0.18 ± 0.06 mG during prenatal ultrasound examination. During patient consultation, the mean ELF-MF exposures of clinicians and pregnant women were 0.10 ± 0.01 and 0.11 ± 0.01 mG, respectively. Mean ELF-MF exposures during prenatal ultrasound examination were significantly higher than those during patient consultations (P < 0.001 by Mann–Whitney U test). Our results provide basic reference data on the ELF-MF exposure of both clinicians and pregnant women during prenatal ultrasound monitoring from 2 different ultrasound devices and patient consultation, all of which were below 2 mG, the most stringent level considered safe in many studies, thus relieving any anxiety of clinicians and pregnant women regarding potential risks of ELF-MFs.

Screening for pre-diabetes using support vector machine model.

The global prevalence of diabetes is rapidly increasing. Studies support screening and interventions for pre-diabetes, which results in serious complications and diabetes. This study aimed at developing an intelligence-based screening model for pre-diabetes that could assist with decreasing the prevalence of diabetes through early identification and subsequent interventions. Data from the Korean National Health and Nutrition Examination Survey (KNHANES) were used, excluding subjects with diabetes. The KNHANES 2010 data (n = 4,685) were used for training and internal validation, while data from KNHANES 2011 (n = 4,566) were used for external validation. We developed a model to screen for pre-diabetes using support vector machine (SVM), and performed a systematic evaluation of the SVM model using internal and external validation. We compared the performance of the SVM model with that of a screening score model based on logistic regression analysis for pre-diabetes that had been developed previously. Backward elimination logistic regression resulted in associations between pre-diabetes and age, sex, waist circumference, body mass index, alcohol intake, family history of diabetes, and hypertension. The areas under the curves (AUCs) for the SVM model in the internal and external datasets were 0.761 and 0.731, respectively, while the AUCs for the screening score model were 0.734 and 0.712, respectively. The SVM model developed in this study performed better than the screening score model that had been developed previously and may be more effective for pre-diabetes screening.

Exposure of Surgeons to Magnetic Fields during Laparoscopic and Robotic Gynecologic Surgeries.

STUDY OBJECTIVE To measure and compare levels of extremely-low-frequency magnetic field (ELF-MF) exposure to surgeons during laparoscopic and robotic gynecologic surgeries. DESIGN Prospective case-control study. DESIGN CLASSIFICATION Canadian Task Force I. SETTING Gynecologic surgeries at the Yonsei University Health System in Seoul, Korea from July to October in 2014. PATIENTS Ten laparoscopic gynecologic surgeries and 10 robotic gynecologic surgeries. INTERVENTION The intensity of ELF-MF exposure to surgeons was measured every 4 seconds during 10 laparoscopic gynecologic surgeries and 10 robotic gynecologic surgeries using portable ELF-MF measuring devices with logging capability. MEASUREMENT AND MAIN RESULTS The mean ELF-MF exposures were .1 ± .1 mG for laparoscopic gynecologic surgeries and .3 ± .1 mG for robotic gynecologic surgeries. ELF-MF exposure levels to surgeons during robotic gynecologic surgery were significantly higher than those during laparoscopic gynecologic surgery (p < .001) after adjustment for duration of measurement. CONCLUSION The present study demonstrated low levels of ELF-MF exposure to surgeons during robotic gynecologic surgery and conventional laparoscopic surgery, hoping to alleviate concerns regarding the hazards of MF exposure posed to surgeons and hospital staff.